Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples
"> Figure 1
<p>Overview of our classification-based change detection framework.</p> "> Figure 2
<p>The Mask R-CNN framework for instance segmentation.</p> "> Figure 3
<p>The MS-FCN framework for semantic segmentation.</p> "> Figure 4
<p>Examples of the simulated change detection dataset. The first row is the original building mask maps, the second row shows simulated buildings added or original buildings eliminated upon the mask maps, where the original buildings are slightly shifted according to a random parallax. The last row is simulated change labels.</p> "> Figure 5
<p>The building change detection network.</p> "> Figure 6
<p>The five sub-datasets in the WHU building dataset with 0.2 m GSD (ground sampling distance). The red box is the main study area with images of 2011 (TA-2011) and 2016 (TA-2016) for evaluating change detection. The yellow box contains images of 2016 (SC-2016) to train the building extraction model for 2016. The green box contains images of 2011 to train the building extraction model for 2011. In the blue box, we simulate changed building samples automatically upon existing building masks of 2016 (SI-2016) to train the building change detection model.</p> "> Figure 7
<p>Examples of the SC-2011, SC-2016, TA-2011 and TA-2016 sub-datasets.</p> "> Figure 7 Cont.
<p>Examples of the SC-2011, SC-2016, TA-2011 and TA-2016 sub-datasets.</p> "> Figure 8
<p>Examples of changed buildings. The top row is images of 2011, middle row is images of 2016 and bottom row is the change labels.</p> "> Figure 9
<p>Examples of building extraction results of the Mask R-CNN and MS-FCN. From top to bottom: image, label, results of the Mask R-CNN, results of the MS-FCN. From left to right: columns 1–3 are from the TA-2016 data and columns 4–5 are from TA-2011.</p> "> Figure 10
<p>The change map of the study area (TA-2011 and TA-2016) with 2007 changed buildings. The red box contains training samples, and the rest are test samples including 1715 changed buildings. The green box expresses only half of the samples used for training.</p> "> Figure 11
<p>Comparison of change detection methods with half of the training samples. (<b>a</b>) Image 2011. (<b>b</b>) Image 2016. (<b>c</b>) Label. (<b>d</b>) Our method with the Mask R-CNN building extraction. (<b>e</b>) Our method with the MS-FCN building extraction. (<b>f</b>) FC-EF. (<b>g</b>) GAN-based change detection.</p> "> Figure 12
<p>Change detection results of different methods on the whole test area. From top to bottom: 2011 and 2016 images; label with 1715 truly changed buildings; results of our method (Mask R-CNN) without change samples; our method (Mask R-CNN) fine-tuned on half of the change samples (green box in <a href="#remotesensing-11-01343-f010" class="html-fig">Figure 10</a>); FC-EF trained on half of the samples; FC-EF trained on all of the samples; GAN-based method trained on half of the samples. We did not list the results of our methods with the MS-FCN and the results with all of the samples because the former looks the same as with the Mask R-CNN, and the latter looks the same as with half samples.</p> "> Figure 12 Cont.
<p>Change detection results of different methods on the whole test area. From top to bottom: 2011 and 2016 images; label with 1715 truly changed buildings; results of our method (Mask R-CNN) without change samples; our method (Mask R-CNN) fine-tuned on half of the change samples (green box in <a href="#remotesensing-11-01343-f010" class="html-fig">Figure 10</a>); FC-EF trained on half of the samples; FC-EF trained on all of the samples; GAN-based method trained on half of the samples. We did not list the results of our methods with the MS-FCN and the results with all of the samples because the former looks the same as with the Mask R-CNN, and the latter looks the same as with half samples.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Building Extraction Network
2.2. Self-Trained Building Change Detection Network
3. Experiments and Analysis
3.1. Data Set and Evaluation Measures
3.2. Building Extraction Results
3.3. Building Change Detection Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Datasets | GSD (m) | Area (km2) | Tiles | Pixels | Building Number | Box Color (Figure 6) |
---|---|---|---|---|---|---|
SC-2016 | 0.2 | 57.744 | 5400 | 512 × 512 | 67,190 | Yellow |
SC-2011 | 0.2 | 22.035 | 2065 | 512 × 512 | 11,495 | Green |
TA-2016 | 0.2 | 19.964 | 1827 | 512 × 512 | 11,595 | Red |
TA-2011 | 0.2 | 19.964 | 1827 | 512 × 512 | 9588 | Red |
SI-2016 | 0.2 | 20.294 | 1892 | 512 × 512 | 21,876 | Blue |
Dataset | Method | Objected-Based | Pixel-Based | |||||||
---|---|---|---|---|---|---|---|---|---|---|
AP | Precision | Recall | TP+FP | TP | TP+FN | IoU | Precision | Recall | ||
TA-2011 | Mask R-CNN | 0.833 | 0.892 | 0.930 | 9993 | 8916 | 9588 | 0.867 | 0.943 | 0.915 |
MS-FCN | 0.773 | 0.922 | 0.837 | 8702 | 8022 | 9588 | 0.869 | 0.934 | 0.925 | |
TA-2016 | Mask R-CNN | 0.858 | 0.922 | 0.929 | 11,684 | 10,768 | 11,595 | 0.897 | 0.956 | 0.936 |
MS-FCN | 0.857 | 0.939 | 0.911 | 11,243 | 10,560 | 11,595 | 0.920 | 0.960 | 0.957 |
Dataset | Extraction Method | Objected-Based | Pixel-Based | |||||||
---|---|---|---|---|---|---|---|---|---|---|
AP | Precision | Recall | TP+FP | TP | TP + FN | IoU | Precision | Recall | ||
simulated | Mask R-CNN | 0.630 | 0.644 | 0.943 | 2511 | 1618 | 1715 | 0.798 | 0.856 | 0.922 |
MS-FCN | 0.609 | 0.659 | 0.896 | 2332 | 1537 | 1715 | 0.798 | 0.839 | 0.943 | |
Half | Mask R-CNN | 0.806 | 0.928 | 0.857 | 1584 | 1470 | 1715 | 0.773 | 0.952 | 0.804 |
MS-FCN | 0.793 | 0.881 | 0.880 | 1714 | 1510 | 1715 | 0.843 | 0.912 | 0.918 | |
FC-EF [62] | 0.027 | 0.200 | 0.114 | 980 | 196 | 1715 | 0.261 | 0.516 | 0.346 | |
GAN [70] | 0.023 | 0.135 | 0.127 | 1616 | 218 | 1715 | 0.232 | 0.538 | 0.290 | |
Full | Mask R-CNN | 0.814 | 0.910 | 0.883 | 1663 | 1514 | 1715 | 0.837 | 0.931 | 0.892 |
MS-FCN | 0.796 | 0.891 | 0.872 | 1679 | 1496 | 1715 | 0.830 | 0.938 | 0.878 | |
FC-EF [62] | 0.254 | 0.519 | 0.462 | 1525 | 792 | 1715 | 0.502 | 0.767 | 0.593 | |
GAN [70] | / | / | / | / | / | / | / | / | / |
Method | AP | Precision | Recall |
---|---|---|---|
Difference | 0.010 | 0.010 | 0.872 |
Distance & IoU 1 | 0.290 | 0.345 | 0.839 |
Distance & IoU 2 | 0.290 | 0.343 | 0.844 |
Erode & dilate | 0.388 | 0.489 | 0.793 |
Erode & intersect | 0.450 | 0.540 | 0.832 |
Our network | 0.630 | 0.644 | 0.943 |
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Ji, S.; Shen, Y.; Lu, M.; Zhang, Y. Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples. Remote Sens. 2019, 11, 1343. https://doi.org/10.3390/rs11111343
Ji S, Shen Y, Lu M, Zhang Y. Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples. Remote Sensing. 2019; 11(11):1343. https://doi.org/10.3390/rs11111343
Chicago/Turabian StyleJi, Shunping, Yanyun Shen, Meng Lu, and Yongjun Zhang. 2019. "Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples" Remote Sensing 11, no. 11: 1343. https://doi.org/10.3390/rs11111343